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Predicting the Ecological Quality of Rivers: A Machine Learning Approach and a What-if Scenarios Tool

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Abstract

Monitoring the ecological status of rivers is essential for protecting freshwater biodiversity and ecosystem health. The main objective of this work was to predict the ecological quality of Greek rivers using a machine learning approach based on the Extreme Gradient Boosting (XGBoost) classifier. We used a dataset that comprises ecological, physicochemical, geomorphological, and sample-related parameters collected from the national monitoring network of Greek rivers as well as climate parameters from the ERA5-Land dataset. More specifically, we developed multiple models that predicted the ecological quality class derived by four quality elements (QEs) that are benthic macroinvertebrates, benthic diatoms, fish, and physicochemical quality. The Shapley Additive exPlanations (SHAP) approach was implemented for quantifying the contributions of the predictors on the quality class. We finally developed a web interface tool that can simulate what-if scenarios to predict the quality class under altered environmental conditions. Our findings showed that total phosphorus, nitrate, and ammonium were important predictors for benthic macroinvertebrates, benthic diatoms, and physicochemical quality, whereas for fish, predictors related with the geomorphology (e.g., altitude and slope) had a higher influence. The SHAP plots revealed the synergistic effect of predictors on the quality classes, highlighting a negative effect of increased nutrients on achieving the good quality class based on macroinvertebrates and diatoms and a positive relationship between altitude and slope with the good ecological quality class based on fish. Furthermore, the web interface could provide a useful tool for water managers to predict quality classes for water bodies under what-if scenarios.

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Data Availability

Data cannot be shared openly but is available upon request from the authors.

Notes

  1. https://dimpolitik-water-quality-myapp-yws3vl.streamlit.app/

  2. Overfitting occurs when the model fits overly closely to its training data (including noise or random fluctuations) and then performs well when applied to other unseen data, thus yielding poor model accuracy.

  3. https://scikit-optimize.github.io/stable/modules/generated/skopt.BayesSearchCV.html

  4. https://imbalanced-learn.org/stable/over_sampling.html

  5. https://xgboost.readthedocs.io/en/stable/

  6. https://shap.readthedocs.io/en/latest/example_notebooks/tabular_examples/tree_based_models/Census%20income%20classification%20with%20XGBoost.html

  7. https://shap.readthedocs.io/en/latest/

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Acknowledgements

The ERA5-Land climate reanalysis dataset was generated using Copernicus Climate Change Service Information [2022]. We thank the reviewers for their useful comments.

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Authors and Affiliations

  1. Institute of Marine Biological Resources and Inland Waters, Hellenic Centre for Marine Research, 19013, Anavissos, Attica, Greece

    Dimitris Politikos, Kostas Stefanidis, George Varlas, Anastasios Papadopoulos & Elias Dimitriou

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  1. Dimitris Politikos
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  2. Kostas Stefanidis
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  3. George Varlas
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Contributions

Conceptualization: D.P., K.S., and G.V.; data curation: K.S.; methodology: D.P.; data analysis and programming: D.P.; visualization: D.P., K.S., and G.V.; writing—original draft: D.P., K.S., and G.V.; writing—review and editing: A.P. and E.D.

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Correspondence to Dimitris Politikos.

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Appendix

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Table 4 Tested hyperparameters along with their optimal values
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Politikos, D., Stefanidis, K., Varlas, G. et al. Predicting the Ecological Quality of Rivers: A Machine Learning Approach and a What-if Scenarios Tool. Environ Model Assess (2024). https://doi.org/10.1007/s10666-024-09980-y

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